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12,237 results for “2019”
Sea ice core temperature and salinity data collected during the 2019 SCALE Spring Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) spring cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
An epidemiological overview of the equine influenza epidemic in Great Britain during 2019: Dataset
<p>This repository contains datasets and code used for the manuscript as titled. All details regarding the data source and considerations that should be noted are discussed in the manuscript.</p> <p><strong>Referencing this dataset</strong></p> <p>Fleur Whitlock, John Grewar & J. Richard Newton (2022) An epidemiological overview of the equine influenza epidemic in Great Britain during 2019 [Dataset]. University of Cambridge. <a href="https://doi.org/10.5281/zenodo.5886153">https://doi.org/10.5281/zenodo.7010228</a></p> <p> </p>
Multi-Sensor Ice Analysis Data: Analysis for Belgica Bank, North East Greenland 2019-20
<p>The intention is that this dataset can be used for machine learning and deep neural network training/validation, and it distinguishes sea ice concentration, type and form derived from manual analysis of a combination of different satellite sensors including ALOS-2, Sentinel-1, COSMO-SkyMed, Sentinel-2, and ICESAT-2. The region chosen for the analysis was the Belgica Bank area offshore of North East Greenland, as this is an area which experiences a wide variety of sea ice, and iceberg, conditions throughout the year. The dataset consists of two parts: 11 days of individual sea ice interpretations, one for each month in the period from April 2019 to March 2020, with the exception of October 2019, and iceberg surveys derived from Sentinel-2 for spring in 2019 and 2020. </p> <p>The dataset includes a user guide issued by MET Norway as report 10/2022 (see https://www.met.no/publikasjoner/met-report) in which the first part describes the data sources, nomenclature, file formats and data in the analysis. A second part of the report compares synthetic aperture radar (SAR) data from both L-band ALOS-2 and C-band Sentinel-1 satellites, and identifies the visible synergies and anomalies. The results confirm that there are variations in backscatter signatures between ALOS-2 and Sentinel-1 data when comparing them for different sea ice situations and conditions. ALOS-2 data in many cases is proven to be a reliable and beneficial source of data when it comes to identifying icebergs, ridges, determining sea ice type, and also distinguishing ice and water compared to standalone Sentinel-1 data.</p>
Social contact data for IDPs in Somaliland (2019)
<p>Social contact data for internally displaced people (IDP) living in Digaale IDP camp in Somaliland. Participants reported all their direct contacts in the 24 hours preceding the survey. This survey was conducted in 2019. See the corresponding paper for more information: <a href="https://doi.org/10.1016/j.epidem.2022.100625">https://doi.org/10.1016/j.epidem.2022.100625</a></p> <p>Data is formatted to be used in the <em>socialmixr</em> package in <em>R</em>.</p>
Programa Nacional de Vivienda en México 2019-2024
<p><strong>Coordinador del Seminario</strong>: Carlos A. Navarrete Ulloa.<br> <strong>Expositor</strong>: Ramona Esmeralda Velázquez García</p> <p><strong>Comité Ejecutivo </strong>PRONACE-Vivienda<br> Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).<br> Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.<br> Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).<br> Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</p> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:<br> SEDATU (2019) Programa Nacional de Vivienda 2019-2024. https://www.gob.mx/shf/documentos/plan-nacional-de-vivienda-pnv-2019-2024 </p>
Data on carbon, nitrogen, and phosphorus forms in a north temperate river, Rivière du Nord, Québec, Canada, from 2017 to 2019
<p>The Rivière du Nord was sampled at 13 sites along its mainstem once per season from summer 2017 to winter 2020. Sites are numbered by river kilometer (RKm) with the outlet being RKm 0. The dataset includes concentrations (ug/L or mg/L as noted) of total organic carbon, dissolved organic carbon, particulate organic carbon, total nitrogen, total dissolved nitrogen, nitrate, ammonium, dissolved organic nitrogen, total phosphorus, total dissolved phosphorus and particulate phosphorus. It also includes fluorescence metrics derived from PARAFAC EEMs: fluorescence intensities (in Raman units) of 5 dissolved organic matter components (C1-C5), and 5 indices (SUVA-254, CDOM, FI, b:a, HIX). We sampled an additional 12 sites to act as endmembers (5 mainly forested sites, 5 mainly agricultural sites, and 2 wastewater treatment plant measures).</p> <p>Data were used to calculate C:N:P stoichiometry in the paper "Different forms of carbon, nitrogen, and phosphorus influence ecosystem stoichiometry in a north temperate river across seasons and land uses" (<a href="https://doi.org/10.1002/lno.11960">https://doi.org/10.1002/lno.11960</a>).</p> <p>Data were used to quantify changes in organic matter composition in the paper "Contrasting seasons and land uses alter riverine dissolved organic matter composition" (<a href="https://doi.org/10.1007/s10533-022-00979-9">https://doi.org/10.1007/s10533-022-00979-9</a>). </p>
InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3
<p>A stack of unwrapped interferograms on Owens Valley, California for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">2019 Ridgecrest earthquake sequence</a>.</p> <p>Sensor: Sentinel-1 descending track 71</p> <p>Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms</p> <p>Processor: <a href="https://hyp3-docs.asf.alaska.edu/guides/insar_product_guide/">ASF HyP3</a> (GAMMA)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
A study of comparative (2019-2023) trends and current acceleration in Particulate Matter (PM2.5) concentration in India
<p><span> For PM<sub>2.5</sub><span> </span>monitoring model, the data was procured from the Central Pollution Control Board’s functional and selected air monitoring stations. The data is available online at the <span> Central Pollution Control Board but in form of daily trends with numerous air quality monitoring stations in an area; monthly and Annual average level especially PM2.5 trends processed from the original data. </span></span></p>
ERA5-Land selected indicators daily aggregates for Africa, 2019
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2019.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
2019_ERPROBA__SeasonalSurfaceWater
<p><strong>Abstract: </strong></p> <p>This dataset was produced by the global component of the Copernicus Land Service and derived from PROBA-V satellite observations. The resolution for Land Cover is 100 meter. This dataset was windowed to E4warning study area. </p> <p><strong>Description:</strong></p> <p>These layers are extracted for the E4warning extent from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, & Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (V3.0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3939050">https://doi.org/10.5281/zenodo.3939050</a>). A nunber of kilometre resolution derivatives have been produced from the original 100m resolution dataset for seasonal and permanent water categories: distance to permanent or seasonal water (erprobapermseaswatdistm1kmll.tif); percentage of seasonal water (erprobavLLC2019pcseaswat1km.tif); percentage permanent water (erprobavLLC2019pcpermwat1km.tif)</p>
CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>- CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m</a> <br>- CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m</a></p> <p>Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management.<br>The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices.</p> <p>Processing steps:<br>To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value.</p> <p>File naming:<br>Until December 2020: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif</code> <br>From January 2021 on: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.<br>NOTE: data for 2023-04 are missing, since they are not available from CLMS</p> <p>Pixel values:<br>0: Sea<br>70: Water<br>255: No water</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 27:17:30N<br>south: 14:43:30N<br>west: 17:04:30W<br>east: 04:48:00W</p> <p>Temporal extent:<br>January 2019 - December 2023 (except: April 2023)</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>Generated using European Union's Copernicus Land Monitoring Service information</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
MODIS NDVI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023).</p> <p>Source data:<br>- MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p><br>The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.</p> <p>For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84.</p> <p>File naming:<br><code>ndvi_filt_YYYY_MM_01T00_00_00.tif</code><br>e.g.: <code>ndvi_filt_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28N<br>south: 14N<br>west: 18W<br>east: 4W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
Surface inherent optical properties and phytoplankton pigment concentrations from the Atlantic Meridional Transect (2009 - 2019): NetCDF format
<p>This dataset is a compilation of particulate inherent optical properties (IOPs) and co-incident high performance liquid chromatography (HPLC) phytoplankton pigment concentrations measured underway on nine Atlantic Meridional Transect (AMT) cruises. The time period of data collection is 2009 - 2019, between Sep-Nov within each year, with measurements collected between approximately 50 degrees South to 50 degrees North. A separate netCDF file is provided for each cruise (AMT 19, and AMT 22-29), including particulate IOPs (absorption, scattering, beam attenuation), pigment concentrations, and associated metadata.</p> <p>A manuscript containing a full description of the dataset, including associated code, will soon be submitted to Earth System Science Data. A Jupytper notebook illustrating data access is provided at: https://github.com/tjor/AMT_ACSpaperplots/blob/main/AMT_DataAccess.ipynb.</p> <p>The data are also released in SeaBASS format: https://seabass.gsfc.nasa.gov/archive/PML/AMT</p>
Trento 1936 - Building 2019
<u>Coordinates</u>: N/A <br><u>Length</u>: 20.25 m<br><u>Width</u>: 12.14 m<br><u>Height</u>: 21.79 m<br><u>Points</u>: 14 <br><u>Vertices</u>: 72 <br><u>Primitives</u>: 24 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.obj</th><th>.glb</th><th>.xml</th></tr><tr><td><a href="https://zenodo.org/api/records/12672447/files/building_2019.glb/content">building_2019.glb</a></td><td></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019.glb/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12672447/files/building_2019.obj/content">building_2019.obj</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019.obj/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12672447/files/11575325_metsmods.xml/content">11575325_metsmods.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12672447/files/11575325_metsmods.xml/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12672447/files/11575325_edm.xml/content">11575325_edm.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12672447/files/11575325_edm.xml/content">Link</a></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12672447/files/building_2019_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12544341">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12672447">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Estimated surface NO2 over Europe for the year 2019-2021
<p>These datasets consist of estimated daily surface NO<sub>2</sub> concentrations over Europe at ~1km spatial resolution in tiff file format generated using S-MESH model and is part of the research article https://doi.org/10.1016/j.rse.2024.114321. Files are zipped into 3 folders each corresponding to a year and can be unzipped from command line using "tar -xvzf filename.tar.gz". Each file represents surface NO<sub>2</sub> during the Sentinel-5P satellite overpass time and the file is named based on the date of measurement. Each tiff file is a single band image with an extent of 25°W-42.5°E & 29.9-74.28°N in EPSG:4326 - WGS 84 projection. The surface NO<sub>2</sub> concentrations are estimated using Sentinel-5P TROPOMI tropospheric column density and a XGBoost machine learning model. The overall median absolute error of the model predictions across Europe is 4.43μg/m<sup>3 </sup></p> <p><strong>Summary</strong></p> <ul> <li>Data: NO<sub>2</sub> concentrations over Europe at ~1km spatial resolution</li> <li>Time Period: 2019-2021</li> <li>Methodology: Using Sentinel-5P TROPOMI NO2 and XGBoost </li> </ul> <ul> <li><span>More information in the article </span><span><a href="https://www.sciencedirect.com/science/article/pii/S0034425724003390#s0155" target="_blank" rel="noopener"><span>https://authors.elsevier.com/sd/article/S0034-4257(24)00339-0</span></a></span><span> or </span><span><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.rse.2024.114321" target="_blank" rel="noopener"><span>https://doi.org/10.1016/j.rse.2024.114321 </span></a></span></li> </ul>
CLDF dataset derived from Bodt's "Lexical Cognates in Western Kho-Bwa" from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bodt, Timotheus Adrianus and List, Johann-Mattis (2019): Testing the predictive strength of the comparative method: An ongoing experiment on unattested words in Western Kho-Bwa languages. Papers in Historical Phonology 4.1: 22-44.</p> </blockquote>
CLDF dataset derived from Sagart et al.'s "Sino-Tibetan Database of Lexical Cognates" from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Laurent Sagart, Jacques, Guillaume, Yunfan Lai, and Johann-Mattis List (2019): Sino-Tibetan Database of Lexical Cognates. Jena: Max Planck Institute for the Science of Human History.</p> </blockquote>
GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci.
<p><strong>Data from article Stocker et al. (in review) *Nature Geosci.*</strong></p> <p>The datasets provided here include:</p> <ul> <li>Site-level GPP model results from the P-model (Wang et al., 2017)</li> <li>Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019)</li> </ul> <p>This data may be used to partly reproduce results presented in Stocker et al. (2019) <em>Nature Geosci</em>. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data.</p> <p>For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts `plot_bias_all.R` and `plot_bias_problem.R`, available from <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="http://doi.org/10.5281/zenodo.1423328">Zenodo</a>, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file `si_soilm_global.Rmd`. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on <a href="http://rpubs.com/stineb/si_soilm_global2">RPubs</a>.</p> <p>The present datasets are prepared by script `prepare_data_openaccess.R ` on <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="https://zenodo.org/record/1286966#.W6TFipMzbUI">Zenodo</a>.</p> <p><strong>Data description</strong></p> <p><em>Site-level data</em></p> <p>Data is provided as CSV files:</p> <ul> <li>`gpp_daily_fluxnet_stocker18natgeo.csv`: Daily data for full time series (not including MODIS GPP)</li> <li>`gpp_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP)</li> <li>`gpp_alg_daily_fluxnet_stocker18natgeo.csv`: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a))</li> <li>`gpp_alg_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects.</li> </ul> <p>Each column is a variable with the following name and units (not all variables are available in all files):</p> <ul> <li>`site_id`: FLUXNET site ID </li> <li>`date`: Date of measurement, units: YYYY-MM-DD</li> <li>`gpp_pmodel` and `gpp_modis`: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files)</li> <li>`aet_splash`: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`pet_splash`: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`soilm_splash`: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless.</li> <li>`flue`: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless.</li> <li>`beta_a`, `beta_b`, and `beta_c`: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless.</li> </ul> <p><em>Global P-model simulation outputs</em></p> <p>GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file `s1b_fapar3g_v2_global.fland.nc`.</p> <ul> <li>`s1b_fapar3g_v2_global.d.gpp.nc`: Daily GPP from simulation s1b.</li> <li>`s1b_fapar3g_v2_global.d.wcont.nc`: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.)</li> </ul> <p>Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com. </p> <p><strong>References</strong></p> <p>Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017).<br> Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004).<br> Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a).<br> Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019).<br> Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).<br> </p>
Derived data and analysis code accompanying Deines et al. 2019, Environmental Research Letters
<p>This codebase accompanies the paper:</p> <p>Deines, JM, AD Kendall, JJ Butler, Jr., & DW Hyndman. 2019. Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environmental Research Letters. DOI: <a href="https://doi.org/10.1088/1748-9326/aafe39">https://doi.org/10.1088/1748-9326/aafe39</a></p> <p>Data and code at time of publication.</p>
SLAFEEL: R scripts and reformatted data analyzed by Alamil et al. (2019)
<p>SLAFEEL: Statistical Learning Approach For Estimating Epidemiological Links from deep sequencing data</p> <p>This archive contains R scripts for running analyses proposed by Alamil et al. (2019; Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases), namely<br> - functions.R that contains R functions required for computations,<br> - influenza.R, ebola.R and potyvirus.R where the analyses are implemented for each case study, and<br> - influenza-format-genomic-data.R giving an example of how to format data to be used in the statistical learning approach.</p> <p>This archive also contains the reformatted data analyzed by Alamil et al. (2019). The datasets that are provided concern swine influenza virus (reformatted from Murcia et al., 2012), Ebola virus (reformatted from Gire et al., 2014) and a wild salsify potyvirus. Two rds files are provided for swine influenza, the first one for the naive chain, the second one for the vaccinated chain. Ebola rds files are compressed into the archive ebolaRDS.zip. rds files can be loaded in the R statistical software with the command "readRDS(filename)", which returns a list. The list contains a "readme" item describing the contents of the list, as well as a "host.table" item providing metadata about host units and a "set.of.sequences" item providing sequencing data formatted in numeric matrices.</p> <p>Murcia PR, Hughes J, Battista P, Lloyd L, Baillie GJ, Ramirez-Gonzalez RH, et al. Evolution of an Eurasian avian-like influenza virus in naive and vaccinated pigs. PLoS Pathogens. 2012;8(5):e1002730.</p> <p>Gire SK, Goba A, Andersen KG, Sealfon RS, Park DJ, Kanneh L, et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science. 2014;345:1369–1372</p> <p> </p> <p>Funded by the ANR - Project name: SMITID (2016-2020) - Grant number: ANR-16-CE35-0006</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.